{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "##第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    'id':np.arange(162542,180542),\n",
    "    'api': ['/front-api/bill/create']*18000,\n",
    "    'count': np.random.randint(1,10,18000),\n",
    "    'res_time_sum': np.random.uniform(550,1058,size = 18000),\n",
    "    'res_time_min': np.random.uniform(85,138,size = 18000),\n",
    "    'res_time_max': np.random.uniform(180,255,size = 18000),\n",
    "    'res_time_avg': np.random.uniform(130,190,size = 18000),\n",
    "    'interval': ['60']*18000,\n",
    "    'created_at': pd.date_range('2017-11-01',freq='T',periods=18000)   \n",
    "}\n",
    "df=pd.DataFrame(data)\n",
    "df\n",
    "df.to_csv('./log.txt',index = False ,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id\\tapi\\tcount\\tres_time_sum\\tres_time_min\\tre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162542\\t/front-api/bill/create\\t8\\t551.2613106...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162543\\t/front-api/bill/create\\t7\\t569.8329854...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162544\\t/front-api/bill/create\\t5\\t806.5190949...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162545\\t/front-api/bill/create\\t6\\t604.1443922...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0\n",
       "0  id\\tapi\\tcount\\tres_time_sum\\tres_time_min\\tre...\n",
       "1  162542\\t/front-api/bill/create\\t8\\t551.2613106...\n",
       "2  162543\\t/front-api/bill/create\\t7\\t569.8329854...\n",
       "3  162544\\t/front-api/bill/create\\t5\\t806.5190949...\n",
       "4  162545\\t/front-api/bill/create\\t6\\t604.1443922..."
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>551.261311</td>\n",
       "      <td>102.860267</td>\n",
       "      <td>222.730933</td>\n",
       "      <td>132.594572</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162543</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>569.832985</td>\n",
       "      <td>105.549721</td>\n",
       "      <td>220.338695</td>\n",
       "      <td>177.494477</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162544</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>806.519095</td>\n",
       "      <td>132.931392</td>\n",
       "      <td>207.676521</td>\n",
       "      <td>171.364576</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162545</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>604.144392</td>\n",
       "      <td>107.122415</td>\n",
       "      <td>241.717019</td>\n",
       "      <td>130.857680</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162546</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>697.659099</td>\n",
       "      <td>98.333639</td>\n",
       "      <td>186.026292</td>\n",
       "      <td>164.185577</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8    551.261311    102.860267   \n",
       "1  162543  /front-api/bill/create      7    569.832985    105.549721   \n",
       "2  162544  /front-api/bill/create      5    806.519095    132.931392   \n",
       "3  162545  /front-api/bill/create      6    604.144392    107.122415   \n",
       "4  162546  /front-api/bill/create      6    697.659099     98.333639   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0    222.730933    132.594572        60  2017-11-01 00:00:00  \n",
       "1    220.338695    177.494477        60  2017-11-01 00:01:00  \n",
       "2    207.676521    171.364576        60  2017-11-01 00:02:00  \n",
       "3    241.717019    130.857680        60  2017-11-01 00:03:00  \n",
       "4    186.026292    164.185577        60  2017-11-01 00:04:00  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt',sep = '\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14252</th>\n",
       "      <td>176794</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>575.533155</td>\n",
       "      <td>136.254723</td>\n",
       "      <td>247.445155</td>\n",
       "      <td>180.123956</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-10 21:32:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5514</th>\n",
       "      <td>168056</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>749.279294</td>\n",
       "      <td>95.001553</td>\n",
       "      <td>197.257798</td>\n",
       "      <td>162.921876</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-04 19:54:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3109</th>\n",
       "      <td>165651</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>668.458242</td>\n",
       "      <td>125.608279</td>\n",
       "      <td>182.753539</td>\n",
       "      <td>154.267546</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-03 03:49:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4758</th>\n",
       "      <td>167300</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>621.830505</td>\n",
       "      <td>87.024319</td>\n",
       "      <td>201.604256</td>\n",
       "      <td>164.131762</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-04 07:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5266</th>\n",
       "      <td>167808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>641.602622</td>\n",
       "      <td>101.781309</td>\n",
       "      <td>195.080661</td>\n",
       "      <td>154.577800</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-04 15:46:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "14252  176794  /front-api/bill/create      1    575.533155    136.254723   \n",
       "5514   168056  /front-api/bill/create      3    749.279294     95.001553   \n",
       "3109   165651  /front-api/bill/create      1    668.458242    125.608279   \n",
       "4758   167300  /front-api/bill/create      1    621.830505     87.024319   \n",
       "5266   167808  /front-api/bill/create      9    641.602622    101.781309   \n",
       "\n",
       "       res_time_max  res_time_avg  interval           created_at  \n",
       "14252    247.445155    180.123956        60  2017-11-10 21:32:00  \n",
       "5514     197.257798    162.921876        60  2017-11-04 19:54:00  \n",
       "3109     182.753539    154.267546        60  2017-11-03 03:49:00  \n",
       "4758     201.604256    164.131762        60  2017-11-04 07:18:00  \n",
       "5266     195.080661    154.577800        60  2017-11-04 15:46:00  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) #随机采样，多次执行，数据不一样，看大概"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 9)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18000 entries, 0 to 17999\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            18000 non-null  int64  \n",
      " 1   api           18000 non-null  object \n",
      " 2   count         18000 non-null  int64  \n",
      " 3   res_time_sum  18000 non-null  float64\n",
      " 4   res_time_min  18000 non-null  float64\n",
      " 5   res_time_max  18000 non-null  float64\n",
      " 6   res_time_avg  18000 non-null  float64\n",
      " 7   interval      18000 non-null  int64  \n",
      " 8   created_at    18000 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() #查看内存占用空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      18000\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                       18000\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('api',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18000 entries, 0 to 17999\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            18000 non-null  int64  \n",
      " 1   count         18000 non-null  int64  \n",
      " 2   res_time_sum  18000 non-null  float64\n",
      " 3   res_time_min  18000 non-null  float64\n",
      " 4   res_time_max  18000 non-null  float64\n",
      " 5   res_time_avg  18000 non-null  float64\n",
      " 6   interval      18000 non-null  int64  \n",
      " 7   created_at    18000 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   18000\n",
       "unique                  18000\n",
       "top       2017-11-10 04:43:00\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>551.261311</td>\n",
       "      <td>102.860267</td>\n",
       "      <td>222.730933</td>\n",
       "      <td>132.594572</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162543</td>\n",
       "      <td>7</td>\n",
       "      <td>569.832985</td>\n",
       "      <td>105.549721</td>\n",
       "      <td>220.338695</td>\n",
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       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162544</td>\n",
       "      <td>5</td>\n",
       "      <td>806.519095</td>\n",
       "      <td>132.931392</td>\n",
       "      <td>207.676521</td>\n",
       "      <td>171.364576</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162545</td>\n",
       "      <td>6</td>\n",
       "      <td>604.144392</td>\n",
       "      <td>107.122415</td>\n",
       "      <td>241.717019</td>\n",
       "      <td>130.857680</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162546</td>\n",
       "      <td>6</td>\n",
       "      <td>697.659099</td>\n",
       "      <td>98.333639</td>\n",
       "      <td>186.026292</td>\n",
       "      <td>164.185577</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17995</th>\n",
       "      <td>180537</td>\n",
       "      <td>8</td>\n",
       "      <td>662.640641</td>\n",
       "      <td>105.212193</td>\n",
       "      <td>252.840637</td>\n",
       "      <td>189.587482</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-13 11:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17996</th>\n",
       "      <td>180538</td>\n",
       "      <td>2</td>\n",
       "      <td>786.979894</td>\n",
       "      <td>125.495486</td>\n",
       "      <td>185.705204</td>\n",
       "      <td>183.861464</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-13 11:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17997</th>\n",
       "      <td>180539</td>\n",
       "      <td>4</td>\n",
       "      <td>788.100317</td>\n",
       "      <td>102.209454</td>\n",
       "      <td>183.680839</td>\n",
       "      <td>183.038853</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-13 11:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17998</th>\n",
       "      <td>180540</td>\n",
       "      <td>8</td>\n",
       "      <td>796.939893</td>\n",
       "      <td>94.173798</td>\n",
       "      <td>211.170991</td>\n",
       "      <td>182.274993</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-13 11:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17999</th>\n",
       "      <td>180541</td>\n",
       "      <td>6</td>\n",
       "      <td>773.781695</td>\n",
       "      <td>110.054054</td>\n",
       "      <td>241.234840</td>\n",
       "      <td>189.744256</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-13 11:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0      162542      8    551.261311    102.860267    222.730933    132.594572   \n",
       "1      162543      7    569.832985    105.549721    220.338695    177.494477   \n",
       "2      162544      5    806.519095    132.931392    207.676521    171.364576   \n",
       "3      162545      6    604.144392    107.122415    241.717019    130.857680   \n",
       "4      162546      6    697.659099     98.333639    186.026292    164.185577   \n",
       "...       ...    ...           ...           ...           ...           ...   \n",
       "17995  180537      8    662.640641    105.212193    252.840637    189.587482   \n",
       "17996  180538      2    786.979894    125.495486    185.705204    183.861464   \n",
       "17997  180539      4    788.100317    102.209454    183.680839    183.038853   \n",
       "17998  180540      8    796.939893     94.173798    211.170991    182.274993   \n",
       "17999  180541      6    773.781695    110.054054    241.234840    189.744256   \n",
       "\n",
       "       interval           created_at  \n",
       "0            60  2017-11-01 00:00:00  \n",
       "1            60  2017-11-01 00:01:00  \n",
       "2            60  2017-11-01 00:02:00  \n",
       "3            60  2017-11-01 00:03:00  \n",
       "4            60  2017-11-01 00:04:00  \n",
       "...         ...                  ...  \n",
       "17995        60  2017-11-13 11:55:00  \n",
       "17996        60  2017-11-13 11:56:00  \n",
       "17997        60  2017-11-13 11:57:00  \n",
       "17998        60  2017-11-13 11:58:00  \n",
       "17999        60  2017-11-13 11:59:00  \n",
       "\n",
       "[18000 rows x 8 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.created_at>='2017-11-01') & (df.created_at<'2018-01-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=18000, step=1)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index#当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 18000 entries, 2017-11-01 00:00:00 to 2017-11-13 11:59:00\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            18000 non-null  int64  \n",
      " 1   count         18000 non-null  int64  \n",
      " 2   res_time_sum  18000 non-null  float64\n",
      " 3   res_time_min  18000 non-null  float64\n",
      " 4   res_time_max  18000 non-null  float64\n",
      " 5   res_time_avg  18000 non-null  float64\n",
      " 6   interval      18000 non-null  int64  \n",
      " 7   created_at    18000 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2017-11-01 00:00:00', '2017-11-01 00:01:00', '2017-11-01 00:02:00',\n",
       "       '2017-11-01 00:03:00', '2017-11-01 00:04:00', '2017-11-01 00:05:00',\n",
       "       '2017-11-01 00:06:00', '2017-11-01 00:07:00', '2017-11-01 00:08:00',\n",
       "       '2017-11-01 00:09:00',\n",
       "       ...\n",
       "       '2017-11-13 11:50:00', '2017-11-13 11:51:00', '2017-11-13 11:52:00',\n",
       "       '2017-11-13 11:53:00', '2017-11-13 11:54:00', '2017-11-13 11:55:00',\n",
       "       '2017-11-13 11:56:00', '2017-11-13 11:57:00', '2017-11-13 11:58:00',\n",
       "       '2017-11-13 11:59:00'],\n",
       "      dtype='object', name='created_at', length=18000)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-11-01 00:00:00', '2017-11-01 00:01:00',\n",
       "               '2017-11-01 00:02:00', '2017-11-01 00:03:00',\n",
       "               '2017-11-01 00:04:00', '2017-11-01 00:05:00',\n",
       "               '2017-11-01 00:06:00', '2017-11-01 00:07:00',\n",
       "               '2017-11-01 00:08:00', '2017-11-01 00:09:00',\n",
       "               ...\n",
       "               '2017-11-13 11:50:00', '2017-11-13 11:51:00',\n",
       "               '2017-11-13 11:52:00', '2017-11-13 11:53:00',\n",
       "               '2017-11-13 11:54:00', '2017-11-13 11:55:00',\n",
       "               '2017-11-13 11:56:00', '2017-11-13 11:57:00',\n",
       "               '2017-11-13 11:58:00', '2017-11-13 11:59:00'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=18000, freq=None)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>2017-11-01 00:00:00</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>551.261311</td>\n",
       "      <td>102.860267</td>\n",
       "      <td>222.730933</td>\n",
       "      <td>132.594572</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2017-11-01 00:01:00</th>\n",
       "      <td>162543</td>\n",
       "      <td>7</td>\n",
       "      <td>569.832985</td>\n",
       "      <td>105.549721</td>\n",
       "      <td>220.338695</td>\n",
       "      <td>177.494477</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:00</td>\n",
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       "    <tr>\n",
       "      <th>2017-11-01 00:02:00</th>\n",
       "      <td>162544</td>\n",
       "      <td>5</td>\n",
       "      <td>806.519095</td>\n",
       "      <td>132.931392</td>\n",
       "      <td>207.676521</td>\n",
       "      <td>171.364576</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:00</th>\n",
       "      <td>162545</td>\n",
       "      <td>6</td>\n",
       "      <td>604.144392</td>\n",
       "      <td>107.122415</td>\n",
       "      <td>241.717019</td>\n",
       "      <td>130.857680</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:00</td>\n",
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       "    <tr>\n",
       "      <th>2017-11-01 00:04:00</th>\n",
       "      <td>162546</td>\n",
       "      <td>6</td>\n",
       "      <td>697.659099</td>\n",
       "      <td>98.333639</td>\n",
       "      <td>186.026292</td>\n",
       "      <td>164.185577</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:00</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 23:55:00</th>\n",
       "      <td>163977</td>\n",
       "      <td>6</td>\n",
       "      <td>981.638679</td>\n",
       "      <td>87.827814</td>\n",
       "      <td>187.950809</td>\n",
       "      <td>161.312874</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 23:56:00</th>\n",
       "      <td>163978</td>\n",
       "      <td>8</td>\n",
       "      <td>847.434553</td>\n",
       "      <td>94.760673</td>\n",
       "      <td>203.750553</td>\n",
       "      <td>161.096663</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 23:57:00</th>\n",
       "      <td>163979</td>\n",
       "      <td>2</td>\n",
       "      <td>1019.992264</td>\n",
       "      <td>96.784963</td>\n",
       "      <td>211.089243</td>\n",
       "      <td>183.911973</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 23:58:00</th>\n",
       "      <td>163980</td>\n",
       "      <td>7</td>\n",
       "      <td>992.804987</td>\n",
       "      <td>114.527061</td>\n",
       "      <td>212.937025</td>\n",
       "      <td>158.278796</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 23:59:00</th>\n",
       "      <td>163981</td>\n",
       "      <td>5</td>\n",
       "      <td>856.402604</td>\n",
       "      <td>118.010517</td>\n",
       "      <td>245.092874</td>\n",
       "      <td>185.868887</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:00  162542      8    551.261311    102.860267    222.730933   \n",
       "2017-11-01 00:01:00  162543      7    569.832985    105.549721    220.338695   \n",
       "2017-11-01 00:02:00  162544      5    806.519095    132.931392    207.676521   \n",
       "2017-11-01 00:03:00  162545      6    604.144392    107.122415    241.717019   \n",
       "2017-11-01 00:04:00  162546      6    697.659099     98.333639    186.026292   \n",
       "...                     ...    ...           ...           ...           ...   \n",
       "2017-11-01 23:55:00  163977      6    981.638679     87.827814    187.950809   \n",
       "2017-11-01 23:56:00  163978      8    847.434553     94.760673    203.750553   \n",
       "2017-11-01 23:57:00  163979      2   1019.992264     96.784963    211.089243   \n",
       "2017-11-01 23:58:00  163980      7    992.804987    114.527061    212.937025   \n",
       "2017-11-01 23:59:00  163981      5    856.402604    118.010517    245.092874   \n",
       "\n",
       "                     res_time_avg  interval           created_at  \n",
       "created_at                                                        \n",
       "2017-11-01 00:00:00    132.594572        60  2017-11-01 00:00:00  \n",
       "2017-11-01 00:01:00    177.494477        60  2017-11-01 00:01:00  \n",
       "2017-11-01 00:02:00    171.364576        60  2017-11-01 00:02:00  \n",
       "2017-11-01 00:03:00    130.857680        60  2017-11-01 00:03:00  \n",
       "2017-11-01 00:04:00    164.185577        60  2017-11-01 00:04:00  \n",
       "...                           ...       ...                  ...  \n",
       "2017-11-01 23:55:00    161.312874        60  2017-11-01 23:55:00  \n",
       "2017-11-01 23:56:00    161.096663        60  2017-11-01 23:56:00  \n",
       "2017-11-01 23:57:00    183.911973        60  2017-11-01 23:57:00  \n",
       "2017-11-01 23:58:00    158.278796        60  2017-11-01 23:58:00  \n",
       "2017-11-01 23:59:00    185.868887        60  2017-11-01 23:59:00  \n",
       "\n",
       "[1440 rows x 8 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-11-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18000.0\n",
       "mean        60.0\n",
       "std          0.0\n",
       "min         60.0\n",
       "25%         60.0\n",
       "50%         60.0\n",
       "75%         60.0\n",
       "max         60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>551.261311</td>\n",
       "      <td>102.860267</td>\n",
       "      <td>222.730933</td>\n",
       "      <td>132.594572</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:00</th>\n",
       "      <td>7</td>\n",
       "      <td>569.832985</td>\n",
       "      <td>105.549721</td>\n",
       "      <td>220.338695</td>\n",
       "      <td>177.494477</td>\n",
       "      <td>2017-11-01 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:00</th>\n",
       "      <td>5</td>\n",
       "      <td>806.519095</td>\n",
       "      <td>132.931392</td>\n",
       "      <td>207.676521</td>\n",
       "      <td>171.364576</td>\n",
       "      <td>2017-11-01 00:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:00</th>\n",
       "      <td>6</td>\n",
       "      <td>604.144392</td>\n",
       "      <td>107.122415</td>\n",
       "      <td>241.717019</td>\n",
       "      <td>130.857680</td>\n",
       "      <td>2017-11-01 00:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:00</th>\n",
       "      <td>6</td>\n",
       "      <td>697.659099</td>\n",
       "      <td>98.333639</td>\n",
       "      <td>186.026292</td>\n",
       "      <td>164.185577</td>\n",
       "      <td>2017-11-01 00:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:55:00</th>\n",
       "      <td>8</td>\n",
       "      <td>662.640641</td>\n",
       "      <td>105.212193</td>\n",
       "      <td>252.840637</td>\n",
       "      <td>189.587482</td>\n",
       "      <td>2017-11-13 11:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:56:00</th>\n",
       "      <td>2</td>\n",
       "      <td>786.979894</td>\n",
       "      <td>125.495486</td>\n",
       "      <td>185.705204</td>\n",
       "      <td>183.861464</td>\n",
       "      <td>2017-11-13 11:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:57:00</th>\n",
       "      <td>4</td>\n",
       "      <td>788.100317</td>\n",
       "      <td>102.209454</td>\n",
       "      <td>183.680839</td>\n",
       "      <td>183.038853</td>\n",
       "      <td>2017-11-13 11:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:58:00</th>\n",
       "      <td>8</td>\n",
       "      <td>796.939893</td>\n",
       "      <td>94.173798</td>\n",
       "      <td>211.170991</td>\n",
       "      <td>182.274993</td>\n",
       "      <td>2017-11-13 11:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:59:00</th>\n",
       "      <td>6</td>\n",
       "      <td>773.781695</td>\n",
       "      <td>110.054054</td>\n",
       "      <td>241.234840</td>\n",
       "      <td>189.744256</td>\n",
       "      <td>2017-11-13 11:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2017-11-01 00:00:00      8    551.261311    102.860267    222.730933   \n",
       "2017-11-01 00:01:00      7    569.832985    105.549721    220.338695   \n",
       "2017-11-01 00:02:00      5    806.519095    132.931392    207.676521   \n",
       "2017-11-01 00:03:00      6    604.144392    107.122415    241.717019   \n",
       "2017-11-01 00:04:00      6    697.659099     98.333639    186.026292   \n",
       "...                    ...           ...           ...           ...   \n",
       "2017-11-13 11:55:00      8    662.640641    105.212193    252.840637   \n",
       "2017-11-13 11:56:00      2    786.979894    125.495486    185.705204   \n",
       "2017-11-13 11:57:00      4    788.100317    102.209454    183.680839   \n",
       "2017-11-13 11:58:00      8    796.939893     94.173798    211.170991   \n",
       "2017-11-13 11:59:00      6    773.781695    110.054054    241.234840   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2017-11-01 00:00:00    132.594572  2017-11-01 00:00:00  \n",
       "2017-11-01 00:01:00    177.494477  2017-11-01 00:01:00  \n",
       "2017-11-01 00:02:00    171.364576  2017-11-01 00:02:00  \n",
       "2017-11-01 00:03:00    130.857680  2017-11-01 00:03:00  \n",
       "2017-11-01 00:04:00    164.185577  2017-11-01 00:04:00  \n",
       "...                           ...                  ...  \n",
       "2017-11-13 11:55:00    189.587482  2017-11-13 11:55:00  \n",
       "2017-11-13 11:56:00    183.861464  2017-11-13 11:56:00  \n",
       "2017-11-13 11:57:00    183.038853  2017-11-13 11:57:00  \n",
       "2017-11-13 11:58:00    182.274993  2017-11-13 11:58:00  \n",
       "2017-11-13 11:59:00    189.744256  2017-11-13 11:59:00  \n",
       "\n",
       "[18000 rows x 6 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 18000 entries, 2017-11-01 00:00:00 to 2017-11-13 11:59:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   count         18000 non-null  int64  \n",
      " 1   res_time_sum  18000 non-null  float64\n",
      " 2   res_time_min  18000 non-null  float64\n",
      " 3   res_time_max  18000 non-null  float64\n",
      " 4   res_time_avg  18000 non-null  float64\n",
      " 5   created_at    18000 non-null  object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 984.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.993611</td>\n",
       "      <td>804.975495</td>\n",
       "      <td>111.473729</td>\n",
       "      <td>217.549198</td>\n",
       "      <td>159.974201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.579394</td>\n",
       "      <td>147.181864</td>\n",
       "      <td>15.203433</td>\n",
       "      <td>21.758574</td>\n",
       "      <td>17.254490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>550.009262</td>\n",
       "      <td>85.011455</td>\n",
       "      <td>180.004998</td>\n",
       "      <td>130.001311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>678.051867</td>\n",
       "      <td>98.326317</td>\n",
       "      <td>198.641189</td>\n",
       "      <td>145.154663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>803.631194</td>\n",
       "      <td>111.498509</td>\n",
       "      <td>217.411908</td>\n",
       "      <td>160.139345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>932.630943</td>\n",
       "      <td>124.572174</td>\n",
       "      <td>236.414375</td>\n",
       "      <td>174.779728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1057.975041</td>\n",
       "      <td>137.997748</td>\n",
       "      <td>254.999530</td>\n",
       "      <td>189.998530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count  res_time_sum  res_time_min  res_time_max  res_time_avg\n",
       "count  18000.000000  18000.000000  18000.000000  18000.000000  18000.000000\n",
       "mean       4.993611    804.975495    111.473729    217.549198    159.974201\n",
       "std        2.579394    147.181864     15.203433     21.758574     17.254490\n",
       "min        1.000000    550.009262     85.011455    180.004998    130.001311\n",
       "25%        3.000000    678.051867     98.326317    198.641189    145.154663\n",
       "50%        5.000000    803.631194    111.498509    217.411908    160.139345\n",
       "75%        7.000000    932.630943    124.572174    236.414375    174.779728\n",
       "max        9.000000   1057.975041    137.997748    254.999530    189.998530"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist() #初步分析count，直方图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#表示接口调用分布情况，反映出每分钟调用的分布情况\n",
    "df['count'].hist(bins = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#切出一天的数据，绘制一天时段的接口调用情况\n",
    "df['2017-11-02']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用count重采样，用一个小时进行采样，没那么多数据点了，图像比较平滑\n",
    "df2 = df['2017-11-02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-02 00:00:00</th>\n",
       "      <td>5.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 01:00:00</th>\n",
       "      <td>4.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 02:00:00</th>\n",
       "      <td>4.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 03:00:00</th>\n",
       "      <td>4.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 04:00:00</th>\n",
       "      <td>5.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 05:00:00</th>\n",
       "      <td>4.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 06:00:00</th>\n",
       "      <td>5.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 07:00:00</th>\n",
       "      <td>5.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 08:00:00</th>\n",
       "      <td>4.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 09:00:00</th>\n",
       "      <td>4.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 10:00:00</th>\n",
       "      <td>4.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 11:00:00</th>\n",
       "      <td>5.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 12:00:00</th>\n",
       "      <td>4.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 13:00:00</th>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 14:00:00</th>\n",
       "      <td>4.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 15:00:00</th>\n",
       "      <td>4.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 16:00:00</th>\n",
       "      <td>5.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 17:00:00</th>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 18:00:00</th>\n",
       "      <td>5.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 19:00:00</th>\n",
       "      <td>3.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 20:00:00</th>\n",
       "      <td>5.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 21:00:00</th>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 22:00:00</th>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 23:00:00</th>\n",
       "      <td>4.933333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count\n",
       "created_at                   \n",
       "2017-11-02 00:00:00  5.033333\n",
       "2017-11-02 01:00:00  4.916667\n",
       "2017-11-02 02:00:00  4.916667\n",
       "2017-11-02 03:00:00  4.966667\n",
       "2017-11-02 04:00:00  5.183333\n",
       "2017-11-02 05:00:00  4.883333\n",
       "2017-11-02 06:00:00  5.433333\n",
       "2017-11-02 07:00:00  5.050000\n",
       "2017-11-02 08:00:00  4.783333\n",
       "2017-11-02 09:00:00  4.683333\n",
       "2017-11-02 10:00:00  4.816667\n",
       "2017-11-02 11:00:00  5.216667\n",
       "2017-11-02 12:00:00  4.933333\n",
       "2017-11-02 13:00:00  5.200000\n",
       "2017-11-02 14:00:00  4.633333\n",
       "2017-11-02 15:00:00  4.816667\n",
       "2017-11-02 16:00:00  5.150000\n",
       "2017-11-02 17:00:00  4.900000\n",
       "2017-11-02 18:00:00  5.066667\n",
       "2017-11-02 19:00:00  3.933333\n",
       "2017-11-02 20:00:00  5.583333\n",
       "2017-11-02 21:00:00  4.900000\n",
       "2017-11-02 22:00:00  4.800000\n",
       "2017-11-02 23:00:00  4.933333"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##折线图和直方图，可以看到业务的高峰时段在什么地方，分不清具体时间，绘制柱状体\n",
    "plt.figure(figsize = (10,3))  #单位是英寸\n",
    "df2['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)#文字旋转角度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析有没有异常时段，访问接口过于频繁，可能就是黑客潮水攻击\n",
    "df['2017-11-02'][['count']].boxplot(showmeans = True,meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:06:00</th>\n",
       "      <td>9</td>\n",
       "      <td>649.585873</td>\n",
       "      <td>103.839672</td>\n",
       "      <td>224.164456</td>\n",
       "      <td>177.922766</td>\n",
       "      <td>2017-11-01 00:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:10:00</th>\n",
       "      <td>9</td>\n",
       "      <td>677.853787</td>\n",
       "      <td>96.997371</td>\n",
       "      <td>253.385156</td>\n",
       "      <td>174.025428</td>\n",
       "      <td>2017-11-01 00:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:11:00</th>\n",
       "      <td>9</td>\n",
       "      <td>1022.919302</td>\n",
       "      <td>122.170293</td>\n",
       "      <td>201.026734</td>\n",
       "      <td>182.428581</td>\n",
       "      <td>2017-11-01 00:11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:22:00</th>\n",
       "      <td>9</td>\n",
       "      <td>876.778082</td>\n",
       "      <td>112.374747</td>\n",
       "      <td>203.937881</td>\n",
       "      <td>156.266332</td>\n",
       "      <td>2017-11-01 00:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:23:00</th>\n",
       "      <td>9</td>\n",
       "      <td>892.332639</td>\n",
       "      <td>135.318764</td>\n",
       "      <td>181.898808</td>\n",
       "      <td>155.887330</td>\n",
       "      <td>2017-11-01 00:23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:01:00</th>\n",
       "      <td>9</td>\n",
       "      <td>1048.512282</td>\n",
       "      <td>117.755490</td>\n",
       "      <td>225.333597</td>\n",
       "      <td>135.310824</td>\n",
       "      <td>2017-11-13 11:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:05:00</th>\n",
       "      <td>9</td>\n",
       "      <td>629.417967</td>\n",
       "      <td>136.932900</td>\n",
       "      <td>219.258723</td>\n",
       "      <td>150.741155</td>\n",
       "      <td>2017-11-13 11:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:12:00</th>\n",
       "      <td>9</td>\n",
       "      <td>566.155682</td>\n",
       "      <td>107.207435</td>\n",
       "      <td>229.359731</td>\n",
       "      <td>168.798423</td>\n",
       "      <td>2017-11-13 11:12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:13:00</th>\n",
       "      <td>9</td>\n",
       "      <td>604.164419</td>\n",
       "      <td>137.540072</td>\n",
       "      <td>249.474563</td>\n",
       "      <td>134.689868</td>\n",
       "      <td>2017-11-13 11:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13 11:17:00</th>\n",
       "      <td>9</td>\n",
       "      <td>830.397356</td>\n",
       "      <td>128.291321</td>\n",
       "      <td>198.988159</td>\n",
       "      <td>151.463356</td>\n",
       "      <td>2017-11-13 11:17:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1975 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2017-11-01 00:06:00      9    649.585873    103.839672    224.164456   \n",
       "2017-11-01 00:10:00      9    677.853787     96.997371    253.385156   \n",
       "2017-11-01 00:11:00      9   1022.919302    122.170293    201.026734   \n",
       "2017-11-01 00:22:00      9    876.778082    112.374747    203.937881   \n",
       "2017-11-01 00:23:00      9    892.332639    135.318764    181.898808   \n",
       "...                    ...           ...           ...           ...   \n",
       "2017-11-13 11:01:00      9   1048.512282    117.755490    225.333597   \n",
       "2017-11-13 11:05:00      9    629.417967    136.932900    219.258723   \n",
       "2017-11-13 11:12:00      9    566.155682    107.207435    229.359731   \n",
       "2017-11-13 11:13:00      9    604.164419    137.540072    249.474563   \n",
       "2017-11-13 11:17:00      9    830.397356    128.291321    198.988159   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2017-11-01 00:06:00    177.922766  2017-11-01 00:06:00  \n",
       "2017-11-01 00:10:00    174.025428  2017-11-01 00:10:00  \n",
       "2017-11-01 00:11:00    182.428581  2017-11-01 00:11:00  \n",
       "2017-11-01 00:22:00    156.266332  2017-11-01 00:22:00  \n",
       "2017-11-01 00:23:00    155.887330  2017-11-01 00:23:00  \n",
       "...                           ...                  ...  \n",
       "2017-11-13 11:01:00    135.310824  2017-11-13 11:01:00  \n",
       "2017-11-13 11:05:00    150.741155  2017-11-13 11:05:00  \n",
       "2017-11-13 11:12:00    168.798423  2017-11-13 11:12:00  \n",
       "2017-11-13 11:13:00    134.689868  2017-11-13 11:13:00  \n",
       "2017-11-13 11:17:00    151.463356  2017-11-13 11:17:00  \n",
       "\n",
       "[1975 rows x 6 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count']>8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#某一天的响应时间，平均响应时间\n",
    "df['2017-11-02']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2017-11-02'][['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lv/Downloads/Python/Practice/jupyter notebook practice/venv/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-02 00:06:00</th>\n",
       "      <td>5</td>\n",
       "      <td>650.025957</td>\n",
       "      <td>102.067129</td>\n",
       "      <td>192.782147</td>\n",
       "      <td>188.210710</td>\n",
       "      <td>2017-11-02 00:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 00:12:00</th>\n",
       "      <td>7</td>\n",
       "      <td>700.264336</td>\n",
       "      <td>113.537252</td>\n",
       "      <td>221.567969</td>\n",
       "      <td>182.616239</td>\n",
       "      <td>2017-11-02 00:12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 00:15:00</th>\n",
       "      <td>2</td>\n",
       "      <td>864.399242</td>\n",
       "      <td>127.705438</td>\n",
       "      <td>185.109733</td>\n",
       "      <td>188.358285</td>\n",
       "      <td>2017-11-02 00:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 00:27:00</th>\n",
       "      <td>7</td>\n",
       "      <td>774.761972</td>\n",
       "      <td>119.783857</td>\n",
       "      <td>194.263410</td>\n",
       "      <td>188.747806</td>\n",
       "      <td>2017-11-02 00:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 00:28:00</th>\n",
       "      <td>3</td>\n",
       "      <td>854.295309</td>\n",
       "      <td>120.281160</td>\n",
       "      <td>207.672283</td>\n",
       "      <td>184.976120</td>\n",
       "      <td>2017-11-02 00:28:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 23:31:00</th>\n",
       "      <td>8</td>\n",
       "      <td>1028.961482</td>\n",
       "      <td>114.772698</td>\n",
       "      <td>205.005059</td>\n",
       "      <td>187.117759</td>\n",
       "      <td>2017-11-02 23:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 23:36:00</th>\n",
       "      <td>1</td>\n",
       "      <td>914.165579</td>\n",
       "      <td>95.069173</td>\n",
       "      <td>206.066767</td>\n",
       "      <td>183.586129</td>\n",
       "      <td>2017-11-02 23:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 23:40:00</th>\n",
       "      <td>3</td>\n",
       "      <td>711.906732</td>\n",
       "      <td>100.745169</td>\n",
       "      <td>223.704466</td>\n",
       "      <td>181.330316</td>\n",
       "      <td>2017-11-02 23:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 23:47:00</th>\n",
       "      <td>3</td>\n",
       "      <td>972.473511</td>\n",
       "      <td>106.950514</td>\n",
       "      <td>227.227226</td>\n",
       "      <td>184.529064</td>\n",
       "      <td>2017-11-02 23:47:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-02 23:51:00</th>\n",
       "      <td>3</td>\n",
       "      <td>871.864457</td>\n",
       "      <td>130.579587</td>\n",
       "      <td>189.014253</td>\n",
       "      <td>187.829203</td>\n",
       "      <td>2017-11-02 23:51:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2017-11-02 00:06:00      5    650.025957    102.067129    192.782147   \n",
       "2017-11-02 00:12:00      7    700.264336    113.537252    221.567969   \n",
       "2017-11-02 00:15:00      2    864.399242    127.705438    185.109733   \n",
       "2017-11-02 00:27:00      7    774.761972    119.783857    194.263410   \n",
       "2017-11-02 00:28:00      3    854.295309    120.281160    207.672283   \n",
       "...                    ...           ...           ...           ...   \n",
       "2017-11-02 23:31:00      8   1028.961482    114.772698    205.005059   \n",
       "2017-11-02 23:36:00      1    914.165579     95.069173    206.066767   \n",
       "2017-11-02 23:40:00      3    711.906732    100.745169    223.704466   \n",
       "2017-11-02 23:47:00      3    972.473511    106.950514    227.227226   \n",
       "2017-11-02 23:51:00      3    871.864457    130.579587    189.014253   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2017-11-02 00:06:00    188.210710  2017-11-02 00:06:00  \n",
       "2017-11-02 00:12:00    182.616239  2017-11-02 00:12:00  \n",
       "2017-11-02 00:15:00    188.358285  2017-11-02 00:15:00  \n",
       "2017-11-02 00:27:00    188.747806  2017-11-02 00:27:00  \n",
       "2017-11-02 00:28:00    184.976120  2017-11-02 00:28:00  \n",
       "...                           ...                  ...  \n",
       "2017-11-02 23:31:00    187.117759  2017-11-02 23:31:00  \n",
       "2017-11-02 23:36:00    183.586129  2017-11-02 23:36:00  \n",
       "2017-11-02 23:40:00    181.330316  2017-11-02 23:40:00  \n",
       "2017-11-02 23:47:00    184.529064  2017-11-02 23:47:00  \n",
       "2017-11-02 23:51:00    187.829203  2017-11-02 23:51:00  \n",
       "\n",
       "[252 rows x 6 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2017-11-02']\n",
    "df2[df['res_time_avg']>180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2017-11-02 23:36:00\t1\t914.165579\t95.069173\t206.066767\t183.586129\t2017-11-02 23:36:00定义为异常值\n",
    "df['2017-11-02'][['res_time_sum',\t'res_time_min',\t'res_time_max',\t'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df['2017-11-02'].resample('20T').mean()\n",
    "data[['res_time_sum',\t'res_time_min',\t'res_time_max',\t'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2017-11-02':'2017-11-12']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
       "           dtype='int64', name='created_at', length=1440)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每天的情况都差不多，下面看看周末和平常是不是一样的\n",
    "df['2017-11-02'].index.weekday#0代表星期一，1代表星期二，5、6代表周六和周日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday']= df.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>551.261311</td>\n",
       "      <td>102.860267</td>\n",
       "      <td>222.730933</td>\n",
       "      <td>132.594572</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:00</th>\n",
       "      <td>7</td>\n",
       "      <td>569.832985</td>\n",
       "      <td>105.549721</td>\n",
       "      <td>220.338695</td>\n",
       "      <td>177.494477</td>\n",
       "      <td>2017-11-01 00:01:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2017-11-01 00:00:00      8    551.261311    102.860267    222.730933   \n",
       "2017-11-01 00:01:00      7    569.832985    105.549721    220.338695   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2017-11-01 00:00:00    132.594572  2017-11-01 00:00:00        2  \n",
       "2017-11-01 00:01:00    177.494477  2017-11-01 00:01:00        2  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>551.261311</td>\n",
       "      <td>102.860267</td>\n",
       "      <td>222.730933</td>\n",
       "      <td>132.594572</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:00</th>\n",
       "      <td>7</td>\n",
       "      <td>569.832985</td>\n",
       "      <td>105.549721</td>\n",
       "      <td>220.338695</td>\n",
       "      <td>177.494477</td>\n",
       "      <td>2017-11-01 00:01:00</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:00</th>\n",
       "      <td>5</td>\n",
       "      <td>806.519095</td>\n",
       "      <td>132.931392</td>\n",
       "      <td>207.676521</td>\n",
       "      <td>171.364576</td>\n",
       "      <td>2017-11-01 00:02:00</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:00</th>\n",
       "      <td>6</td>\n",
       "      <td>604.144392</td>\n",
       "      <td>107.122415</td>\n",
       "      <td>241.717019</td>\n",
       "      <td>130.857680</td>\n",
       "      <td>2017-11-01 00:03:00</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:00</th>\n",
       "      <td>6</td>\n",
       "      <td>697.659099</td>\n",
       "      <td>98.333639</td>\n",
       "      <td>186.026292</td>\n",
       "      <td>164.185577</td>\n",
       "      <td>2017-11-01 00:04:00</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2017-11-01 00:00:00      8    551.261311    102.860267    222.730933   \n",
       "2017-11-01 00:01:00      7    569.832985    105.549721    220.338695   \n",
       "2017-11-01 00:02:00      5    806.519095    132.931392    207.676521   \n",
       "2017-11-01 00:03:00      6    604.144392    107.122415    241.717019   \n",
       "2017-11-01 00:04:00      6    697.659099     98.333639    186.026292   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2017-11-01 00:00:00    132.594572  2017-11-01 00:00:00        2    False  \n",
       "2017-11-01 00:01:00    177.494477  2017-11-01 00:01:00        2    False  \n",
       "2017-11-01 00:02:00    171.364576  2017-11-01 00:02:00        2    False  \n",
       "2017-11-01 00:03:00    130.857680  2017-11-01 00:03:00        2    False  \n",
       "2017-11-01 00:04:00    164.185577  2017-11-01 00:04:00        2    False  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断是否周末，是不是5，6\n",
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    4.995016\n",
       "True     4.990625\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对weekend 进行分组， 对count列求平均值\n",
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0             5.057407\n",
       "         1             5.055556\n",
       "         2             5.061111\n",
       "         3             4.994444\n",
       "         4             5.161111\n",
       "         5             4.983333\n",
       "         6             4.985185\n",
       "         7             5.198148\n",
       "         8             4.985185\n",
       "         9             4.837037\n",
       "         10            5.164815\n",
       "         11            5.072222\n",
       "         12            4.895833\n",
       "         13            4.879167\n",
       "         14            4.933333\n",
       "         15            4.839583\n",
       "         16            4.954167\n",
       "         17            5.010417\n",
       "         18            5.122917\n",
       "         19            4.897917\n",
       "         20            4.904167\n",
       "         21            4.906250\n",
       "         22            4.987500\n",
       "         23            4.916667\n",
       "True     0             5.004167\n",
       "         1             5.008333\n",
       "         2             5.008333\n",
       "         3             4.970833\n",
       "         4             4.850000\n",
       "         5             4.779167\n",
       "         6             4.941667\n",
       "         7             4.916667\n",
       "         8             4.791667\n",
       "         9             5.387500\n",
       "         10            5.079167\n",
       "         11            4.770833\n",
       "         12            4.891667\n",
       "         13            4.975000\n",
       "         14            5.033333\n",
       "         15            5.150000\n",
       "         16            5.291667\n",
       "         17            5.191667\n",
       "         18            4.825000\n",
       "         19            4.762500\n",
       "         20            4.879167\n",
       "         21            5.212500\n",
       "         22            5.029167\n",
       "         23            5.025000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#周末调用平均次数多，4.99\n",
    "#周末哪个时段调用次数比较高\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mamrR55aSKAV7e8P0twd1HF1TMXErKVogNt1uSejWuIW+GIOjUQD2b24r63kiUlOBLivkkomhm6WLV2bq2zioDXoerFmEt+zo4NLVxapi3uNz8Ywh72mzPPTKf+mZ2F3Qz6FtHdpD11RMqZCL5X3Ws3Tx9GgUEdjTGyr7uaGgr2Yeeibkkomhu6MWXRv0PFgJ0cPbO5hfSlUch1NKrfDQrf+r8dCt+Htbs49D2yKMXF1kzAUtx5rGw05SFOrb/j84GmVHZ6ttUa5swoHaDbnIfA4zVS7uqEXXBj0PI9OLdIcDmc60Sr9155dSLCZSmRh6OOAj6PdUFUPPTsYc2tYBwLO6wUhTAcU6RcElBr2ChKhFLSV0o7EEHoHWpuWyRai+iKJatEHPw/DVBQY6mjPfupXG0S3P2Qq1iAg94aAzIZdmP9dvaaPJ6+H4BR120ZSPncYiqJ9BX1xKMTQ5X3ZC1CIcqGXIJUko4MPjEQBaAz7agj5GtYfuPkamF9kSaWZLxDLolf2SLMPdHQpmtnWHA9WFXKzsetBPwOflui1tDS8B8MVnLvLA8eF6L2PDYaexCOpn0M+MRVEK9ldo0Gvpoc8uJjLxc4v+SHPVVXHVog16Dum04tLVGAMdLXSHA/g8UrGHnt32b9ETDjjkoRu3eoe2dvD8yNWGHm335987w2d+dL7ey9hwxJIpfB7B53WnQT9tVrhU7KHXNCmazNSgW2xuDzI6q0MurmJ8Ls5SKs2Wjma8HqG3imkkyx76skE3BLqqj6FbnXA3b48QS6Q51aANRrOxBMPTi0w4oHGjKY94Il002djS5MXnqZ+E7uBolKDfw/YyZHOzCQX8tStbjCUyJYsWfe3NXNYeuruwRLkGOoxwS38kmCljLJfxaJwmr4dI1tT0nnCA2VgyE78sl9lFI3ZneVVWYrRRwy5WnfGEA5IImvIwphUVNgH1ltAdHI2ypyeM14xTl0s46GMpVZupRXlDLu1BJueXKv5sO4E26DlYJYsDEcugN1dc5TIWjdEdDiCy/AdZbXPRbCyRKZUC44+oty3Asw2aGD1t3lnEEumaSp1qVhNLpEoKXtXToJ8ejVYcboHlpp9axNGjBUIuUN/JRbYMuogMichJEXlORI7m2b9fRJ4QkbiIfNT5Za4dlje+xfTQ+9qbGZ2JVdT+PB6N0xUOrNiWqUWvMMSQ6xmICIe2djSsh/7S5Wjm8cTcUh1XsvEwQi7FTUC9NNEn5+JMzMUrTohCbQW6jHmiK0Mu/VUWUThBOR76EaXUTUqpw3n2TQG/Avw3Z5ZVP4anF9nU2kSLWV+6JRIkkVIVxXjHo/FMDbpFRs+lQhldw0Nf6RncvD3ChamFhoxDn7o8i99r3ME04vobGTd76INVJkQhy6A7fOeXTivm4smMRK+F5aHXMzHqSMhFKTWmlHoGqL/WZpVYJYsWmVr0ChKjY3kMek+mW7Syb3FrSko2NzdoHD2dVgyORrllu7H+CT1Ae02JJ0t76HUz6FccMOgZxUVn1x+NG3pKbTlJ0f72xvHQFfCQiBwTkfsqfTERuU9EjorI0fHx8UpPU1OGpxcyCVHIvo0q71t3KZlman4p45FbdIYCeKTaGPpKz+D6Le34PNJwcfTzUwssJlK8fo+hHOnENCeNfWKJVMmW+np66Jtam1ZUiJWL9TlxOuQSzdFxsWhu8hJp8ddVz8WuQX+dUupm4G3AR0TkDZW8mFLqfqXUYaXU4e7u7kpOUVOUUoxcXemhWwNgyzXok/PLk4qy8XqEzlDltej5sutBv5fr+tsaTqjLSoi+9ppORLSHvtYYVS6lDfrs4tpL6J4eNVr+swsKyqVWIZdlHRf/qn2b24J17Ra1ZdCVUiPm/2PAA8CttVxUvTBKjtIrPPT2Zj8tTd6yb6OsGHmuhw5GXXolHrqlhZ57qwdG+eLzwzMkG6jB6NTlWTwCB/ra6GxtYlwnRdeUmI2kaHuzn7QyZJvXirQ11KKKcAvUbq7osp7S6s9hvbtFSxp0EWkVkbD1GLgXeKHWC6sHlg76lo7lYbQiYs4LLM9Dz3SJ5jHoPW2VeeiWFnquhw5waFuExUQq013XCJwajbKrO0TQ76Wrwi85TeXEk/aSogAzC2sXdhmeXmRhKVW1QbfKFp330POHXMDqFnWxQQd6gR+KyAngaeAbSqlvi8iHReTDACKyWUSGgV8HfkdEhkWkPEV6F5CpQc/y0MH61i3PoI/lafu36A4FKlJczNZCzyWTGG2ggRenLs9mytK6QgFd5bLG2PHQ2+rQ/n961AjFVWvQAz4vTV6P4x56NFY45NLfHmSqjs1Fq+8ZclBKnQVuzLP9k1mPR4EBZ5e29oxcNbpEt+Qa9Pbmsj1fy2B3tub30CfmlkinVUatzQ7ZWui5DHQ00xUKcPz8ND97+/ay1loPrJb/n751G2CEpoYm5+u8qo2F3bJFWNshF1bJ4t4KZXOzMYZcOLt2K+SS2/oPK3XRyx1s7QS6UzSL4elF2oK+Vd+8/ZFmxqPxslqIx6NxNrU20ZRHya4nHCSVVkwtlBczzh1Mm42IcPO2SMNUulgf2mv7jBu5rlCTbv9fY0ppuUB9BLpOX4mydVNzJqlZDeEaKC5ajlV+g17fyUXaoGcxMr24In5uUcm8wHw16BaVTi4qFrsDIzE6NLnA1Lz7k4uWmNj+vuWQi27/XztSacVSykZStGXtDfrgaJR9vc5EbGsxV3Q2lqC1yZtXpbLPrJCrl0iXNuhZ5JYsWljbyhHpGo/G81a4wHKitNzE6GyR2B3AzdsiAA0x8OLU5SiRFj+b24wvyy6z3li3/68NS0lLC91dHno8meLcxHxVLf/ZhAI+x4dcR2OrS4ct+jLdotqg1xWlFMPTi6sSolDZbVRxg26cr1wZ3Vwt9Fyu7Te8mjNjc2Wdtx5YCVGrzti6VjoxujYsTysqbgJam7x411BC95WxOVJpVXVC1KJWIZd84RYwekI6WvwVz1CoFm3QTWYXk8zFk3kNerndokqpoga9UoGuXC30XMJBP+Ggj8t1+mOyS8ps+T/Qt3xbbXnounRxbYiVGBBtsdYSulZuxSkPPRz01yQpWuguGUxd9Do1F20Ig76UTJf0hi/m6KBnE/R72dTaZFvPZWYxwVIqvapL1KK5yUs44CtboCtXCz0f/e3NjNRZZL8UF8yW/wObswx6uAnQHvpakRkQXcJDh7Vt/x8cjdLk9WQGtFdLLWLo0ViyYMgFMPtW6vMZrD6NvMZYo9b8RYyaUopXxub4wZkJfnBmnKfOTZFIpfneb9zN1k2rk56QJZsbyb+/PxK07aGPFWkqsugOByry0PN1p2VTzjrrhZUQzfbQO1sNjRvd/r82ZEIuJWLoYCTh18qgnx6Nck1PqOjnuxxC5hg6pVRVMgLZzMYSXNNd+AunLxLkWJ3yWA1n0B8+NcaH//4YHS1+ukIBusMBukLGv85QE0MT8/zgzEQmKbGzq5V3Huzji0eHeeilK/zc63bmPW+hpiKLvvZmLkwu2FpjsbZ/i+5wgPGyPfTCyRiL/kgzz7m8uei02fK/pzeU2eb1CJt0+/+aEbeSonY99DJLbCtlcDTKHdd0Ona+cNBHIqVMZcnSX152mF1MrJLOzaavvZmrCwkWl1I0NznzmnZpOIO+q7uVX33jHiZMAfyJuSVODF9lIhpnfilFpMXPndd08fo9XbxuTxcDZhniiYszfPel0YIGfWR6kRZTLS0fWyLNPPnqpK01js8ZXybFPPSetiAnh8szvKVid2AY9OmFBAtLyYymu9t46fJyy382uv1/7SjHQ29v9nNhDZq+ZhYSjM7GHGkosggHlvVcnDDoSiljQHSBwgRYWUSxqztU8Lha4M5PfBH29obZ++b8v/DFpRRNPk/eGYRvvraX//P9V7m6sESkpWnVfks2t9BtWV97kGg8acuo2vLQKzBes4vJjPpjIZbVIWPs7lnbPya7nLo8y82mBno23WHd/r9WWAY9YMPItTf71iTkYrX8O5UQhWWBrrl4sujn0S6LiRSptCqZFAWjW3StDfq6Soo2myVW+Xjztb2k0opHBsfy7i9Ug27RX0bDwHg0TrPfW7TTractwPxSivkyamRteejtlem3rxWzsQQjVxfzfmi1nsvaUW5SdDaWrHkXrxNDLXIJBYzPS9ShIRfLXaLFk6JAXRKj68qgF+OGLe30hAN896UrefePXF1cpeGSTcbztVGLPhaN09MWKJqE6a6gTM9uDB3q13pcitOXV7b8Z9MVamI8qtv/14K4zbJFMAx6yhy7VksGR6OEg76MQXSCjOKiQ5UuGfmNIiEXaxRdPcqHN4xB93iEN13by/cHx1dpsszFk1xdSGTi7fnoK8PzHYvGSk5asVQY7XaLFtNCz2ZzexARXFu6aN1WH8hr0APEk7r9fy2IJ6xOUXseOtS+W3RwNLqi2cwJrLtkp7pFiymeWgT9Xjpbm7hch27RDWPQAd58oJf5pRRP5CQ3MzroRUIuPeEAXo/YDrnkk81deT6zW9SmjG4xLfRs/F4PPeGAa0Mupy7PEmnx05tPVjis2//XCruNRbA2Bl0pxaADQy1ycdpDz0jnlvgcbm4Pag+91txxTSctTd5VYZfhIk1FFj6vh95wwH7IpUBTkUW5Al12PAOL/kiza0MuL12OcmBzW14vTHeLrh3Lrf/26tChtgb90kyMaCzJvs3OjlGwYt2OxdCLSOdmU69u0Q1l0IN+L3ft7eafT11ZMSMx01RUxKCDvUEXsUSKaKx0Rj3S7MfvFdshl2Ja6PnX6b6QSyqteHk0mlFYzGVZoEsb9FqTSYqWEXKppSb6M+emALiu31mD3howvrCcCuPZdazq1S26oQw6wJsO9HJlNs4Ll2Yy24anFwn4PCXj3n2R0t+6lndZyqB7PEJXKGC7/b+YFnou/e1BRq4uui65eH5y3mj5zxM/By3QtZbEEil8HikqI2GxFiGXB09coq89yE0DEUfPG/B5afJ5nIuhxwproWcz0NHMzGKCu//0EX75c8f568fO8uTZyZrnhxquDr1a7tnfg0fguy9d4aD5xzMybZQslkrG9EeCfOeFWNFJQ1ZM3E7Na08Z7f+ltNBXrrOZpWSayfmljNfrBqypTwcK3FZvam3S7f9rRDyZtpUQhdob9Kn5JR57eZyfe93OsiZ42aXNQcXF2ViCgM9TMlT1U6/ZSjKtODk8w7Pnp/l/Jy4BIALXdIf416/dUZPJYhvOoHe0NnF4xya++9IVfuPefYARQy8VbgGjxnspZRjKQga72HDoXLrDwUz8vhSltNBXrDOrZt5NBv3U5Vm8HlnR8p/Ncvu/Nui1JpZI2e6cDAV8NZXQ/ebJyyTTinff1F+T8zsp0GVI55b+DEZamvjIkd2Znyfm4pwcmeHk8AzPD88QcEirJpcNZ9AB7r22lz/8xikuTi2wdVMLI1cXM1rixciW0S1k0JeFuUrX0naHAzx30Z6ITykt9GyyB3LcMNBu6/xrwanLs+zqai1qSIz2f13lUmtiNsbPWYgIbcHadYs+eOISu3tCeXsTnMAS6HKC2VjC1mcwl65QgCP7ejiyr8eRdRRiw8XQwegaBSPsEkukmJhbKlqyaGFn0MXYbByPGOGDUvSEA0zOL5E0FSSLUUoLPd863Va6eOpylP0lPrS6/X9tiCVTtoS5LAwJXefjv5euLvL0uSnec2O/o/Xn2YQCPseqXKKxpK275HqxIQ369s5W9vSE+O5LV7JUFgs3FVkse76FE6Pj0ThdoUBBCYJsetoCKGWv7tqOFrrFptYmAj6Pqwz6zKLR8n+gQIWLhRboWhviiZQtYS6LWmmiW7HlWoVbwChddCyGvpgomRCtJ+5dWY1587W9/NVjZ3nJ1Oa2E0OPtPgJ+j1FGwbGojHbIkDZ7f+bS7Q729FCtxARttioyFlLTpiSvoUqXCy6Qk1MzMUd1a92EqUU5ycXeH5khqGJefIVEnk98OO3DGS6i91IPJkuy0OvlSb61567xI1bI2zvdGagRT7CAWdDLnZsRb2wZSFEZAiIAikgqZQ6nLNfgD8D3g4sAB9USj3r7FKd5c3X9vKXj77K3z95HijeVGQhIkaNd5GQy/hc3A9+KTwAACAASURBVFZCFAwJXbAqY4rHuu3ouGTTFwmWNdS61jx44hLhgI87dhXXuu4OL7f/20k+1ZqJuThPn5vi+eEZTo5c5eTwTCZBXYyHT4/x1X/3Wld+KYGZFC3TQ7fuZp3ilbEoL12e5Xffea2j583FyRi620Mu5XjoR5RSEwX2vQ3YY/67Dfg/5v+u5caBCN3hAE+fm8LnEVtJTDAqXYo17YzNxrmuz14ispxuUTtKi9n0tzfz/ZfHbR9fSxaXUnzr5GXecbCvZCJuubloqe4GXSnFu/7XD7k8E8PvFfZvbuOdN/ZzcEs7Nwy0s6cnjC9PaO0LRy/y2189yXdeHOWt1/fVYeWliSXSdIXsf/xrEXJ58LlLeATeebC218gaFO3EXZ/hWLk3sOHUyt4D/J0yOlmeFJGIiPQppS47dH7H8XiENx3o4XNPX6Q/0mwr5g1GLfqjg/kNZSqtmJgrPBw6FyvkYqdb1I4W+sp1NjM+F2cpmabJZr1xrXjopVHml1K879BAyWOz2/93OjRXslLOTcxzeSbGb75lHz//+p0EbHq0P3nLAH/zw3N8/NuDvPFAr2Pj1JyknLJFWDboToXClFJ87cQlXntNV+ZOtVaEAoZaZCyRrmqCUCyRIp5Mu9pDt/uXpoCHROSYiNyXZ/8W4GLWz8PmthWIyH0iclREjo6P1997fNMBo9rFToWLRV/7sqHMZWp+ibSipDCXRZPPQ0eL35ZAV7ke+pZIM0rBlToovuXy1WdH2BJp5radm0oe66Zu0aNDRknpW67bbNuYg6H78+/ftp9zE/N8/ukLtVpeVcSS5Rv0VFoxv5QqfbANTgzPcH5yoabJUAtryEU0Xt0dRkaYy8VJUbsG/XVKqZsxQisfEZE3VPJiSqn7lVKHlVKHu7u7KzmFo9y5u4vWJi87ukpXuFgUM5SWYbYbQwdztqgtD738GDpQ9zj6WDTGD86M895D/ba6AN2k5/LM0BQdLf6iA4ELcc/+Hm7buYk/e/iMK+WA4wn7naLgfLfo154bocnn4a3Xb3bkfMXIHkNXDdGY/W7temHrN6qUGjH/HwMeAG7NOWQE2Jr184C5zdUE/V4+f98d/H9v2mv7OX2RwjXeYzZ1XLLpCQdLhlzsaqFnk90EVU8efO4SaYWtcAsst/+7oXTx6PlpDu/YVFGIQUT47bcfYGJuifsfO1uD1VVHJSEXMOZ+Vksqrfj685e5Z1/PmoQvnJLQLadbu16UNOgi0ioiYesxcC/wQs5hDwIfEIPbgRk3x8+zuWGgvawYXsZQ5lS6JFJphiaMQbp2E6xgz0O3q4W+Yp1Zcw3ryQPHRzg40G57vqnV/l8LD/03v3SCTz9+ztax49E45ybmec2O1bNP7XLT1gjvONjHXz92ljEXhL6yiZVZtuikh/7k2UnGo3HeswbhFlhuxqv2Tsnq1m70OvRe4AHTS/EB/6iU+raIfBhAKfVJ4JsYJYuvYJQt/pvaLLf+WIbyr75/li88c5GJuSUm5uJcNT0Xn0fK89DbAoxF40UFv8rRQrdobvLS0eKva8hlcDTKi5dm+b13lVeWVov2/5mFBF9+dpij51v54J07Sx5/7Lwh53p4R+m4fzF+6y37eOjFUT7x8Bn+y/tuqOpcTpFOK5aS6bLKFp3URP/acyOEAj6O7K9tG7xFJoZetYfu/pBLSYOulDoL3Jhn+yezHivgI84uzZ00Nxma6ucn50mmfOzuDnH7rk10hQJ0hwPs39xW1q3s1o4WlpJpxoo0F5WjhZ6NHf32WvLV48P4PMK7bizPE6tF+//TQ1MoZVSunJuYL1lB88zQNAGfh+v7q9PC2d7Zys/ctp3PPnmeD925g909zk7kqYS4mdCvxEOvVhM9lkjxrRdGect1m8v6nFRDm0NDLqINEHJx772Di/nMh3JTCJWzvdNIyA5Nzhc26GVooWfTH2nmwqQ9NUenSaUVXzt+ibv2dpet+NgVCnB2fN7R9Tzx6iQ+j5BMKx4dHGNnV3Ev/ejQFDdtjThS8vnL9+zmK8eG+fi3B/nrDxwu/YQakxkQXU5jUYszHvqjg+NEY8k1C7fAxgq5uK9AdoOxfZPhKRYzvOVooWezpY4e+pNnJxmdjfG+m1dVr5bE8tCdHNDx5NlJbt25iWu6W3mkQB+BxcJSkhcuzfKaKsMtFp2hAB+++xq++9IVnhmacuSc1ZCZVlSGhxxq8uGR6g36/3v+El2hJl57TfGOYScJOZYUTeD1CC1V1LLXGm3Q60x/JIjPIwxNFvZIK82u97UHicaTGQ9/LfnqsyOEA75MrX85dIWaMu3/TnB1YYlTo7PcvquTI/t6ePLsJAtLhc/93MWrpNKKW6pIiObyoTt30tsW4I+/earuk6SW54na//h7PFK1notSiideneSuvT22ROacwu/1EPRXP7XIaPv3uVbOAbRBrzs+r4eBjmbOT9nx0MuPocPaly4uLqX49guXefsNpVv98+H0sOinzhnx8zuu6eTI/h6WkmmeeHWy4PFHh6YRgZu3OWfQm5u8/Js7d/Lshau21DVrSSxpf0B0NtW2/58Zm2Nqfonbdjlz51MOoUD1iovl9oLUA23QXcC2ztbiIZcytNCzyZ5ctJZkWv0rCLfASj0XJ3ji1UmCfg8HB9o5vKOD1iYvjwyOFTz+maEp9vWGM4lAp7ASrGeuRB09b7lYIZdyGougeoP+1FnjS/T2nWsXbrEIOyDQNRtLujp+Dtqgu4IdnS0MTc4XvBUvRws9m+zJRWvJV8xW/1srjEE73f7/5NlJDm/fRMDnJeDzcufuLh45PZ73eidTaZ49P+1Y/DybvZuNWvzBOhv0eKI+HvqT56boaw+yddPay88aY+iqCz3OLpYnv1EPtEF3Ads2tRCNJTO17LmUo4WeTXc4gM8jaxpyGZuN8cMz47zv0JaKB/462f4/Nb/E6dEot2fd5h/Z38PI1UVeGZtbdfzp0SjzSykOOxg/t+gOBYi0+Hn5yurXXUtiSSspWt7Hv63ZX3HZolKKp85OcdvOyjpvqyXswKBot0vngjbormCHKe5fKDFaaezO6xF624Jr2i364Amz1b/CcAs42/7/9DnjNv+OrKqKu/cZOkL5wi5HzSqUWnjoIsLenrALQi6Gh16O4BhU56GfnZhnYi7ObSX08GtFyIEhF5XOE11LtEF3AVYt+oUCidFylRaz2RJpXtOQy3dfusJ1/W1c022v1T8fRvu/M81FT7w6SbPfyw1bIpltfe3N7N8c5pHTq8sXnzk/zZZIcyb/4DR7N4cYvBKta6VLrMqQSyVrf+qs8UVpR3GzFoQc8NCN8XPaQ9eUYOsmw6CfL5AYnV1MVuwZ9EWCaxZyUUpxejTKjVsjpQ8uQVeoyZH2/yfPTnF4R8eqBqEj+3t4ZmhqRfegUoqjQ1Pcst35cIvF3t4w0ViSK7P1Ex+LV5EUTaYVCxVI6D51bpLucKBuGvdtQX9VnaLJVJr5pZQOuWhKE/R76WsPFg65VOGh90eaGZ2JkUrX3iMcnY0xs5jgwObq29u7wwHGq/TQJ+fiDF6Jcnue2/wj+3pIphWPv7I8hGt4epErs/GqBLlKsbfXuDYv1zHsEq+ibBHKby6qd/wclkMuld4ZWeEaHXLR2GLbppaCpYvV1L/2R5pJmpOUas3py4aR2l9iELQdukMBJqqMoT91zrjNvyNPV+LN2yKEg74VYReri7NaQa5iOGnQH3t5vCI52+VO0fI9dCjfoF+YWmB0Nla3+DkYIZe0gsVEZQM6LD0lHXLR2GJ7Z0ve5qJKtNCz2bKGgy5Ojc4Cy0arGrocaP9/4tVJWpu83LBltcCWz+vhDXu7eWRwLPMazwxNEw76HFl/ITa1NtEVaqraoE/NL/GBv3maD33mmYzHbZdqYuhQvkG3vlhvr1P8HJb1VyqNoy/rKWkPXWOD7Z2tjEfjzOdk4ivRQs+mr33tukUHR6NsiTQ70pDjRPv/k2cnObxjU8GZnkf29TAWjfPSZeOLyIqf250vWyl7e8MMVlm6aCXQj52f5j/+0wtlffHFkim8Hil71mnFBv3sFJtam2xr4teCUJVTixpBOhe0QXcNhSpdKtFCz6aa9v+vHBvmLx55xfbxpy9H2e9A/Byqb/8fj8Y5MzaXN9xicddeo3zx0cFxpueXODM2V5NyxVz29oZ5pcpKl+Fp4+/krddt5otHh/n0j4ZsPzdW5vg5i8o99ElurXDyk1NkphZV6CBkJKx1yEVjB6sW/XxOYrRSLXSLtqCPUMDHpTLb/xOpNH/8rdP85SOv2EqoxpMpXh2fY3+fMwZ9uVu0skqXp8z683wJ0ezXODjQziOnxzh23hgIfbiGFS4We3pDzC+lqgqDXZwynvsnP3mQN1/byx9+4xQ/PDNR4lkG8TIHRFu0VaCJPnJ1keHpxbrot2QTChhrr1Rx0fLQdeu/xhbbOvOXLlaqhW4hIvS1l1+6+P3BcSbm4swvpTgzVjre++rYPMm0Yt/m6hOiUH236BOvThIK+Li+v/h67t7Xw7MXpnn49BX8XnGk5LIU+xxIjA5PL9DR4qct6Od//tRNXNPdykf+8dnMGMRixBJpghV46OGADylTQtfSb7mtDvot2SzH0CsrXaxUwnqt0QbdJbQF/XS0+FclRp34Q+qPNK+agVqKLx27SLPpxR2/cLXk8afNhKgTJYtQfcjlybOTvGZHR0n9myP7ukkr+NLRYa7f0r4mU3T2ZAx65XH04elFBjoMJyAU8PGpD7wGEfiFvzta0miVOyDawuMR2oLldYs+dXaK9ma/Y6G4SsnE0CsMuURjSUSMLzU3ow26i9je2bo65OLA2Kv+SHNZiouTc3EePjXGv7xtGx0tfo5fmC75nMHRKE1ej2ONI1b7fyUe+thsjFfH54vGzy0ODkToaDEaZtYifg5GLLq3LcDLo9V56AMdy92s2zpb+Mt/eTNnJ+b5tc8/VzRMFkukCVT4xVVu+/9T5yZ5zY5NFev6OEW4yiEXs7EEoYCv7u+jFNqgu4jtnS2rQy4VaqFnsyUSZHJ+KVOuVoqvPXeJZFrxLw5v5dC2Dlse+qnRKHt6Q44NLqim/f+Js6Xj59mvYyVH1yJ+brG3N8zLNkJZ+VBKmR76SnmC1+7u4vfedS0Pnx7jf3735YLPjydTFSVFoTyDfmU2xtDkwgphtHpRdZXLovuFuUAbdFexfVMLl64usmSq4UHlWujZlFu6+KVjwxwcaGff5jCHtkY4MzZX8kN8+vIs+xy+rTba/8s36E+enSIc8HGdzQHPP37LAAMdzWsa593bG+aVsbmKOnjH5+LEk+mMZEQ2P3v7dt5xsI9P/fBswXPHE+mym4osyjHoT7okfg5G30Gz38tcvLIYejSWcH1CFLRBdxXbO1tJq+WSNKhcCz2b5dLF0mGXF0ZmOHV5lp+8ZQCAQ+bUnueHC3vpU/NLjEXjHHAoIWphtP+XX+VizQ+1W0/++j3d/PBj92QGIa8F+3rDxBJpLhaZVFWI4WnjiznXQwcjCf763V3EEmlGpvN/gccqrHKB8gz6U+eML9ZrSySm14pQFUMuDKXFdeShi4hXRI6LyNfz7NsuIg+LyPMi8qiIDDi7zI2BVYuenRitVAs9G2vQhZ3E6JePDdPk9fCuG42p7Ae3tiNSPDFqJUSdKlm0KLf9fymZ5sVLM5ybsBc/ryd7eo0mm0oqXZYN+moPPfvchaqTYokUwTKlcy3K0UR/6uwkh3fUvlHLLuFA5YqLRsjF/R56OSv8VeAUkO/r9r8Bf6eU+oyI3AP8MfCzDqxvQ7HdqkWfmId9xjYn5hj2tgcQKR1yiSdT/NNzI7z5ul4iLU2AkYzd0xMqmhjNaLg47KFnt/9nN6Wcm5jn4VNXOHNljivRGFdm44zNxpicX/bmX3tNl6NrcRqr0uXM2Bz3Xlfecy2vfksBid/d3cvnfmOeId0xB0Iuub+TXMajcV4dn+cnD2+t6HVqQTVDLmZjCfYH61upYwdbBt30uN8B/BHw63kOuTZr+yPAPzmyug1GV6iJliZvHg+9OoMe8HnpCgVKGvTvnRrj6kIiE26xOLS1g++8NFrwQ3x6dJbO1qZMM5BTWO3/s4tJzoxF+e6pK/zzS1d4ddyoBOoOB9jcFmRLJMihbRF6w0F62wLs6Gp1zW1+IUIBH1sizQxWUOkyPL1IZ2sTrQXyKu0tfnrCAc4UKIuMJVJlD7fInLvZTyKlWEykaGkqbD4sobNb66jfkks1IZdoLNkQIRe7HvongN8CCn1FnQB+DPgz4H1AWEQ6lVKFR6trViEiq1QXZxeT9JsCW9XQH2kuObnoS8eG2dwW5PV7uldsP7QtwheOXmRociFvWeLp0ajj4RZY7hZ93Z98j2gsic8j3L6rkw/csYM3HugpGHJoFPb2hioMuSzkjZ9ns6c3xCsFQi7xZHUeOhjNRcUM+lNnJ2kpIIxWL0IBHxPR8nMW6bQi6kDocy0ouUIReScwppQ6JiJ3Fzjso8D/FpEPAo8BI8CqGjkRuQ+4D2Dbtm0VLnl9s6OzdUXs06lbvS2RIE+fm+byzGKm6iWbsdkYjw6O8eG7rlkV87QSo89dnF5l0FNpxctXovzMbdurXmMuNw5E2NXVyg0D7bzpQC937etuiNIxu+ztDfP4K5MkU+mykt4j04scKCFRvKcnzBePXsx7V1VpYxGsNOj5/o4snjpnCJ2VKwBWS8IVDrmYX0qSVu6XzgV7SdE7gXeLyBDweeAeEfn77AOUUpeUUj+mlDoE/Adz26osmlLqfqXUYaXU4e7u7tzdGozE6MWpxUzJmRMxdIB/fccOFpeSvOt/Pc6x81Or9n/1+AhpBT9xy+p89u6eEKGAL29i9PzkPLFEuiadgLu6Q3zvo3fzZ+8/xLtu7F9XxhwMg76USjNUQAc/H+l0/hr0XHb3hFhYSnEp565MKUU8WV1jEVBUh33aHMxdr3FzhQgFfBV1imaa+1w+3AJsGHSl1G8rpQaUUjuA9wPfU0r9q+xjRKRLRKxz/TbwN46vdIOwrbOFpVSa0dlY1Vro2dy2q5MHPnInrQEv77//Sb7wzIXMPqUUXz42zC3bO9iVZxao1yPcuLU9r0E/PVqbhOhGwNJdL2do9PhcnKVUmoE8NejZ7DGlal8ZWxlHjycrG25hYUdx8eTIDAA3r2Gjlh3CwcqmFkWr1FNaSyq+HxKR/yQi7zZ/vBsYFJGXgV6M5KmmArJVF6vVQs9lb2+Yr33kTm7f1cnHvnKS33/wRRKpNM9dvMorY3OrkqHZ3LQ1wqnLsyzmzJM8fXkWjyyXymnss7snhAgMlmHQrR6F0jH0/F8WVrdwNUlRKG7QrbzAvhoOCqmEcNCHUpQ9E3VZ8dT9Br0s108p9SjwqPn4d7O2fxn4spML26hsMz2vC5MLmcdOegaRlib+9oOv4b9+6zSf+uE5BkejdIUDBP0e3nGwr+DzDm3tIJlWvHBpZoXmyenRKDu7WtdE1Gq90dzkZdumloLVKPmwZHO3ljDom1qb6GxtqouH/vKVKF2hJjpDzlY9VYsloRuNJQtWCOXDqrvXnaKasumPNOP3CkOTC1VroRfC5/XwO++8lv/+kzdy7MI0/+/EJd52fV/RpM9N2wxZ2dx6dKPCRYdbKmVPT7isShfLQ98SKV3hs7snxJkcg54ZP1ehhx4OGhK6xZqLXr4yV9MxfpUSygy5KC8xGo1vgJCLpjZ4PcLWjhYuTM1XrYVeih+/ZYAv3Hc7t+/axC+8flfRY7tCAbZtalkRR5+LJ7kwtcB+F354G4V9m0Ocm5hfod9TjOHpRbpCAZqbShvkPb0hzuRMRloeEF2ZQfd4hHDAV9BDV0px5krUlQY9XKFAlyWZ0eVwn0Ut0AbdhWwzVRfXQlT/0LYOPn/fHbYacQ5ti6ww6FZTjPbQK2dvb5hkWnHOxmAKwFaFi8WenjCzseQKgbPlAdGVf/TbWwrruYxcXWR+KeXKnEqlg6JPj0YZ6GiuSiBvrdAG3YXs6Gzl/ORC5kPjllu9Q1sjjM7GuGxqwmQMep2HFzQye3qMa2c3MXrRRlPR8rktTZflsEu1SVEoLtDl1oQoZIdcyjPog6Ozrnw/+dAG3YVs29TCXDyZ0UZ3S/2r1WBkeemnR2cJBXy2DYxmNbu6W/F6xFbpYiqtuHR10XaH7G5LpCvr3NUmRaGUQTe+PPa40ABaHnY5Qy6WkmnOjs87Lg1dK7RBdyGW6uLzZj2vW271DvS10eTz8NxF06BfjrJvc7iu09wbnaDfy/bOFluJ0bFojERKsXWTvS/Q7lCA9mZ/Xg+9mqqkUh56b1sgUw3jJqyk/2wZ3aKvjs+Zs3K1QddUiKW6+MLITNVa6E7S5PNww5Z2jl+YRinFqdFZHW5xgL09YVvzRUvJ5uYiIuzJqXSJOeah5/dyX3ZpQhSyPPQyQi6DDdY45w5LoVnB1k3NiBiDI9wmCHRoa4Tnh2e4OLVINJbUCVEH2Ls5bEooFG94sWRzywlxGSJdzsbQLU303I7LdFrxypg7SxbBqCBrafKWFXI5PRrF7xV2dTszK7fWaIPuQgI+L/2m8JHbutMObesgnkzzwPERAA5oD71q9vaGSCvj9r4YlodeSAc9H7t7wkzNLzFpzma1YuiBKj30pVQ6UwJpcXF6gVgizV4XVrhYhALlSegOjs5yTXfIVSJjxWiMVW5AatEl6gSHzAYjSwtmrzboVWNVUJSKow9PL9ATDpQV/96dU+kSdyiGDqu7Ra3whFs9dCh/yMXgaLRh4uegDbprsRKjbqlwsehrN4ZIXJqJsSXS7LovnEZkR1crQb+HExdnih53ccp+DbpFrkhXtZ2iUNigW18abqxwsQgF/bYVF2cWE1yaiWmDrqkeKzHqNoMpIty01fDSD9RgqMVGxO/1cMeuTh4dHCt63PDVhbKHevS1B2lt8mYZ9DQeAb+38sqkYh76loi7G3DCAR9zNqtcrDumRkr8a4PuUpY9dHcZdFiuR2+UzH8jcGR/D0OTCwU7RpOpNJevxmyXLFqICLt7w5mhKdZwi2pKTQsZdKPCxb3xcygv5GJJQ+9roL9zbdBdynIM3X3ezmFT5/o6l8/tbCTu3tsDwCOn83vpV6JxkmlV0di9PT2hjKJjPJkm4KvuY5/PoCdTRgOOm+PnUF5SdHB0lnDQR3979SMg1wpt0F3Kzq5WQgEfW0sMMqgHt2zv4B9+/jbuvW5zvZeybtjW2cI13a08UiDsUknJosWenhBj0TgzC4mqxs9Z5DPoQ5MLLKXS7jfoQZ/tssXB0Sj7ehurcU4bdJfSGvDx+Mfu4cduLjx0ol6ICHfu7lo1e1RTHUf29fDU2SkWllYbnHKbirKxhLJeGY8SS6arNuhWx2W2QbfkBdxu0MNBP3NLSdLp4lOLlFKcbrAKF9AG3dW0t/i10dxAHNnfw1IqzY9emVy1b3h6ARHoj5R/+28JgJ25Mkcskao65OL1COGgb4Um+stX5hBZLpN0K+GAMbVoPs+XZjaXZ2JG45w26BqNphIO7+igtcmbN+wyPL1IbzhYUYfnlkgzQb+HM2NzjoRcYLWey8tXomzb1GJLp72e2FVcHGzAhChog67RuIaAz8udu7t4dHB8VVv9xSn7srm5eDySmV7kRFIU8ht0607AzWw2E5yltHMyFS4uDyHlog26RuMijuzvYeTq4qrRceUMtsjHnp4wr1yJEq+Bh76UTHNuYt71JYsAd+zqpLXJy7dfuFz0uJevRNncFqS9xX1lw8XQBl2jcRF37+sGVpYvJlNpRmdjVVU87e4JcWkmxsTcUlVKixbZBv3cxHzDSMwG/V7eeKCX77x4hWSq8Ni/RkyIgjboGo2r6GtvZv/m8Io4+uWZGKm0qtJDN7znkauLjnvoVkdlI4RcAN5+w2am5pd4+txU3v2JVJpXx+YaLiEK2qBrNK7jyP4ejg5NZwYxXJy2atAr99Cz9VWq0XGxyDXoHqFhJGbv2ttDs9/Lt14Yzbt/aGKepVR6fXvoIuIVkeMi8vU8+7aJyCPm/udF5O3OLlOj2Tgc2ddDMq14/MwEkF2DXrmHvrWjmSYzGVqNdK5FW7OfpWSaWCLFy1eipsCYuytcLJqbvNyzv4dvvzhKKk89+nLL/zo26MCvAqcK7Psd4ItKqUPA+4G/rHZhGs1G5eZtEcJBXybsMjy9iEeMcEyl+LwednUZHrRTIRcwmovOXJljb4OEWyzeev1mxqNxjp2fXrVvcDSK16wMajRsGXQRGQDeAXyqwCEKsAo224FL1S9No9mY+Lwe3rC3m0fM8sXhqQU2twUzHnalWGGXoENliwBjs3GGJucbThf/yP4eAj4P3zy5utrl9GiUnV2tVU11qhd2f7OfAH4LKJQW/n3gX4nIMPBN4JfzHSQi94nIURE5Oj4+Xu5aNZoNw5F9PYxH47x4adYsWaxe08dKjAYc9NCPX5wmrWiIksVsQgEfd+3t5tsvjK6SARi8MtuQ4RawYdBF5J3AmFLqWJHDfhr4tFJqAHg78FkRWXVupdT9SqnDSqnD3d3dFS9ao1nv3LXX+Hw8OjjG8HTlTUXZWCEEJ0MuVqWI2zVc8vH2G/oYnY1x/OLVzLa5eJKLU4vsb8D3A/Y89DuBd4vIEPB54B4R+fucY34O+CKAUuoJIAh0ObhOjWZD0R0OcHCgnYdeusLobIwBB1Q3LS+62UGD/szQFH6vsKOzMSpcsrnnQA9NXs+KJiOrBHPdeuhKqd9WSg0opXZgJDy/p5T6VzmHXQDeCCAiBzAMuo6paDRVcGRfD88Pz5BW1VW4WFzTHeJPfvwgb7+hetljy6BfmY2zs6u16vh+PWgL+nn9ni6+eXI0I7Vgabg06vCWin8LIvKfROTd5o+/AfyCiJwAPgd8UOWKUWg0mrI4sr8n89gJgy4i/IvXbCXS0lT1ubInabl5ZucexgAADINJREFUhmgp3nr9ZkauLnJyxJjnOjgapaXJ68j1rgdljcNRSj0KPGo+/t2s7S9hhGY0Go1DHNzSTmdrE5PzS2x1ICnqJF6PEA74iMaTDSdglc2br+3F5xG+eXKUgwMRTo/Osrc3jKdBZasb7z5Jo9kgeDzCXXu78XkkoxLoJiwvvdEqXLKJtDTx2t1dfOuFyyilGByNNmTLv4U26BqNi/noW/Zx/wduwe9130fViqM3csgF4O3Xb+b85AKPvjzO9EKiYROioA26RuNq+iPN3LO/t97LyEt7s58mn4ftLpx7Ww73XrcZr0f4xD+fARpPAz0b942U12g0DcE1Pa2IGJ2tjcym1iZu37WJx83Rf43soWuDrtFoKuIP3n096XVSzPbW6/t4/JVJukIBOkOBei+nYhr7q1Wj0dQNr0dcGduvhLdc14sIDZ0QBe2hazQaDT3hIL/6xj0N21BkoQ26RqPRAL/2pr31XkLVrI/7JY1Go9Fog67RaDTrBW3QNRqNZp2gDbpGo9GsE7RB12g0mnWCNugajUazTtAGXaPRaNYJ2qBrNBrNOkHqNVhIRMaB8xU+vQuYcHA5jYi+BvoagL4GG/H9b1dKdefbUTeDXg0iclQpdbje66gn+hroawD6Gmz095+LDrloNBrNOkEbdI1Go1knNKpBv7/eC3AB+hroawD6Gmz097+ChoyhazQajWY1jeqhazQajSYHbdA1Go1mnVBTgy4izSLyfRHxisgOEVkUkeey/jUVee7dIvJ1h9ezU0SeEpFXROQL1uuLyC+JyIccfB23ve9fMt+zEpGurO0iIn9u7nteRG42t3eLyLerfE23XYN/EJFBEXlBRP5GRPzm9ppdgzxrsK7JjVnXYUpEzpmP/9nJ1yuwhptE5EkReVFETmZdh4dFpL1Gr1nX9y0iPSLyqIjMi8gncvYFRORT5t/GaRF5r7n910TkA7VcV01QStXsH/AR4FfNxzuAF8p47t3A1x1ezxeB95uPPwn8O/NxC3B8Hb/vQ+Y6hoCurO1vB74FCHA78FTWvr8F7lxH1+Dt5vsU4HNZv/uaXYNi1yRr26eBnyhwvM/ha+AHTgI3mD93AR7z8c8BH3Py9Vz0vkPAncAvAZ/I2fdHwO+bjz1AZ9ZzjtXietTyX61DLj8DfK3YASJyq4g8ISLHReRHIrIvzzF3ZX2zHxeRsLn9N0XkGdOz+oMSryPAPcCXzU2fAd4LoJRaAIZE5Nby32JeXPO+AZRSx5VSQ3l2vQf4O2XwJBARkT5z3z+Z76NS3HYNvmm+TwU8DQyYu2p5DXKxc03eZHqTXwdOishuEXkua/+/F5HfMR/vEZHviMgxEXlMRErNUHsbhpE6CaCUmlBKpc19XwP+ZaVvrAR1fd9KqTml1ONALM/uDwIfN49LK6UmrecAl6w7tkahZgbdvKXelWNIrsn6cP6Fue008Hql1CHgd4H/kud0HwU+opS6CXg9sCgi9wJ7gFuBm4BbROQNRZbUCVxVSiXNn4eBLVn7j5rnrgoXvu9ibAEuZv2cfU0qvh5uvgZmiOFnASucUpNrkOd1812TQhwGflEpdaDEcfebx90C/Dbwv0scv9dYijwkIs+KyG9YO5RSE0BYRCI21mcbl7zvQmvrApaAPzavxxdEJLul3rHf/1pRyyHRXcDVnG2vmh/MbNqBz4jIHkBh3Bbm8jjwP0TkH4CvKqWGzQ/1vcBx85gQxof8sQrXOwbsr/C52TTa+y7EGNBf4XPdfA3+EnhMKfUDG8dWcw1yyXdNCvGEUupCsQNMw3s78BXj5hMo/Xn2YYQebsPwVh8Ro3X+++b+caCvjHXawQ3vuxA+jHDgo0qpXxOR3wL+BPg35v4xc3/DUEuDvggEbRz3n4FHlFLvE5EdwKO5Byil/quIfAMj3vm4iLwFI+b5x0qpv7K5nkmM22mf6aUPACNZ+4PmmqvFbe+7GCPA1qyfs69JNdfDlddARH4P6Ab+bdbmWl2DXOxeE4D5rMdJVt5JB81tAkzk+ZIsxjDwfSusICLfAm4GLIPu5Pu1cMP7LsQYsMByOOhLGGG27Nd0+nrUlJqFXJRS04BXREr9MttZ/gB9MN8BInKNUuqkUurjwDMYnvR3gA+JSMg8ZouI9JiPHxaR7HAKZuz0EeAnzE3/mpVxvb3ACzbfXkHc9r5L8CDwATG4HZhRSl0291V8Pdx4DUTk54G3AD+dFTeGGl2DXMq4JrmMAv0i0mE+9x1Z57ssIu8DEBGPiNxoPv4JEfnPec71LeAmMapOfMAbgJfM53gxvOmiHnK5uOR9F1pbGuOaWGGVN2JeDxPHfv9rRa2Tog8BrytxzJ9gxLCOU/iO4dfEKDd7HkgA31JKPQT8I/CEiJzESHaGRcQD7Aam8pznY8Cvi8grGDH1/5u1707guzbfVylc9b5F5FdEZBjD+3xeRD5l7vomcBZ4Bfhr4BeznnYE+Ebpt1oQV10DjKqmXvM5z4nI75rba3kNcrFzTVaglIph5BaOms/PNjjvBz4sIieAF4F3mtt3A7N5zjUJ/DlwDHgOeFIp9R1z92uAH+Z82TlFXd83gPn3/yfAz4nIsCwn4H8T+CPz7+v9wG9lPe0O4OFy1l13allCg3E799lavkae17we+B9lPueQk+tslPdd4nyPAR36GlR+Dep1TTDKMjvLfM5fAHfVaD2ufd9FzvUa4G9rvWan/9Vcy0WMhp3PKKVSNX2hKhCRNwNnlL1MvN1zuv59F8LM9N+plPqnkgcXP8+GvwZ5zuvKayIiP6+U+lTpIys+vyvfdyHMXM0pVSJJ6za0OJdGo9GsE7SWi0aj0awTtEHXaDSadYI26BqNRrNO0AZdo9Fo1gnaoGtcixhiTY5PdBeRIcmSEa4lIhIRkV8sfeSq5/2+iHy0gue9V0SuLfd5mvWBNugajU3M7spyibCyWanWvBfQBn2Dog26xjHEkLT9FfPx/xSR75mP7xFjwMS9YsjlPisiX8pq3b9FjAEIx8SQRe3LOa9HRD4tIn9o/lzoPEMi8gfm9pMist/c3imGwuCLZpeskAcReav53BMi8rC57fdF5LMi8jjwWTEGdvypLEv3/lvzuJApO2C99nvM0/5XlpUm/zTrOq2S/hWR/yAiL4vID4FVUsI5a/0F8xwnROQrItIiIq8F3g38qfl619j+5WnWB/XubNL/1s8/DBW8L5mPf4ChO+4Hfg9DduExoNXc/zEMyVw/8COg29z+U8DfmI8fNc/5OeA/mNu68p3HfDwE/LL5+BeBT5mP/zzrmHdgKDt25ay9G0NGd6f58ybz/9/HaJVvNn++D/gd83EAozV9J4Z0QVvWGl/B+OLYQdZwDwyVyPvNfR7g6xiaKrdgDJ9oAdrM53+0yLXuzHr8h1nv+9MUGByh/63/f7VUW9RsPI5haJO3AXHgWQyN69djiGBdi6GYCNAEPIHhiV4PfNfc7gUuZ53zr4AvKqX+yPz59gLnsfhq1lp+zHz8BuuxUuobIjKdZ+23Y8jqnjOPy9aDeVApZanu3QscFBFL5K0dQ7p3GPgvYuiypzE01XvzvE4h6d8w8IAyhq0gIg/meW4215t3LBHzHN8pcbxmA6ANusYxlFIJETmHoZz4I+B5DIGr3cA54LtKqZ/Ofo6I3AC8qJS6o8BpfwQcEZH/rgzBJsl3nizi5v8pnPv7zpZ1FQxveIUBFZEPYnj5t5jXYYj8srF5pX9F5NfKXNOngfcqpU6Yr313mc/XrEN0DF3jND/AmDL0mPn4wxje6JPAnSKyG0BEWsUYHTYIdIvIHeZ2v4hcl3W+/4uhiPhFMylZ6DzFeAxzvJqIvA3osHbIstzuk8AbRGSnuX1TgXN9B/h3sjxcea+ItGJ46mOmMT8CbDePj2J439nPzyf9+xjwXjGkbcPAu0q8pzCGjKyflWPycl9Ps4HQBl3jND/AmHrzhFLqCsZknB8opcYxPPfPiSFV+gSwXym1hKFR/3Ex5FCfA16bfUKl1P/A+FL4LMagklXnKbGmP8Aw1i9ihF4ugJFsxZTbNdd3H/BVcx1fKHCuT2FIuT4rIi9ghIR8wD8Ah8WQ8/0Axng9lCFZ+7gYEsB/qgpI/yqlnjVf8wSGRvczJd7TfwSewpjodDpr++eB3xRj/qpOim4wtDiXZsMiItcDH1JK/Xq916LROIE26BqNRrNO0ElRjcbFiMhfYEzTyubPlFJ/W4/1aNyN9tA1Go1mnaCTohqNRrNO0AZdo9Fo1gnaoGs0Gs06QRt0jUajWSf8/+6tdccFGUXTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#周末和非周末具体时间对比，绘制图形，否则不直观\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.057407</td>\n",
       "      <td>5.004167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.055556</td>\n",
       "      <td>5.008333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.061111</td>\n",
       "      <td>5.008333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.994444</td>\n",
       "      <td>4.970833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.161111</td>\n",
       "      <td>4.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.983333</td>\n",
       "      <td>4.779167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.985185</td>\n",
       "      <td>4.941667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.198148</td>\n",
       "      <td>4.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.985185</td>\n",
       "      <td>4.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.837037</td>\n",
       "      <td>5.387500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.164815</td>\n",
       "      <td>5.079167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.072222</td>\n",
       "      <td>4.770833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.895833</td>\n",
       "      <td>4.891667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.879167</td>\n",
       "      <td>4.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.933333</td>\n",
       "      <td>5.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.839583</td>\n",
       "      <td>5.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.954167</td>\n",
       "      <td>5.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.010417</td>\n",
       "      <td>5.191667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.122917</td>\n",
       "      <td>4.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.897917</td>\n",
       "      <td>4.762500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.904167</td>\n",
       "      <td>4.879167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.906250</td>\n",
       "      <td>5.212500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.987500</td>\n",
       "      <td>5.029167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.916667</td>\n",
       "      <td>5.025000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend        False     True \n",
       "created_at                    \n",
       "0           5.057407  5.004167\n",
       "1           5.055556  5.008333\n",
       "2           5.061111  5.008333\n",
       "3           4.994444  4.970833\n",
       "4           5.161111  4.850000\n",
       "5           4.983333  4.779167\n",
       "6           4.985185  4.941667\n",
       "7           5.198148  4.916667\n",
       "8           4.985185  4.791667\n",
       "9           4.837037  5.387500\n",
       "10          5.164815  5.079167\n",
       "11          5.072222  4.770833\n",
       "12          4.895833  4.891667\n",
       "13          4.879167  4.975000\n",
       "14          4.933333  5.033333\n",
       "15          4.839583  5.150000\n",
       "16          4.954167  5.291667\n",
       "17          5.010417  5.191667\n",
       "18          5.122917  4.825000\n",
       "19          4.897917  4.762500\n",
       "20          4.904167  4.879167\n",
       "21          4.906250  5.212500\n",
       "22          4.987500  5.029167\n",
       "23          4.916667  5.025000"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#周末和非周末数据叠加\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
